The present invention is directed in general to monitoring performance and operational parameters and fault-related information on a railroad locomotive or other complex electromechanical system, and more specifically, to a method and apparatus for on-board monitoring of performance and fault-related parameters and transmission of the data collected to a monitoring and diagnostic site.
A railroad locomotive is one example of a complex electromechanical system comprised of several complex subsystems. Each of these subsystems is built from components which over time will fail. When a component does fail, it may be difficult to determine the cause of the failed component because the effects or problems that the failure has on the subsystem are often neither readily apparent in terms of their source nor are they typically unique.
The ability to automatically diagnose problems that have occurred or will occur in the locomotive subsystems has a positive impact on minimizing locomotive downtime. It is known that cost efficient operation of a railroad requires minimization of line-of-road failures and locomotive down time. Failure of a major locomotive subsystem can cause serious damage, costly repairs, and significant operational delays. A locomotive break-down while in service is an especially costly event, requiring the dispatch of a replacement locomotive to pull the train consist and possibly rendering a track segment out of service until the train is moved. As a result, the health of the locomotive engine and its constituent subsystems is of significant concern.
Previous attempts to diagnose problems once they have occurred on a locomotive usually involve performing inspections by experienced personnel who have in-depth individual training and experience in working with locomotives. Typically, these experienced individuals use available information that has been recorded in a log. Looking through the log, they use their accumulated experience and training in mapping incidents occurring in locomotive systems to problems that may be causing the incidents. If the incident-problem scenario is simple, then this approach works fairly well. However, if the incident-problem scenario is complex, then it is very difficult to diagnose and correct any failures associated with the incidents.
Currently, computer-based systems are being used to automatically diagnose problems in a locomotive in order to overcome some of the disadvantages associated with relying completely on experienced personnel. Typically, a computer-based system utilizes a mapping between the observed symptoms of the failures and the equipment problems using techniques such as table look ups, a symptom-problem matrices, and production rules.
There is also no automatic or systematic mechanism for the identification of incipient locomotive problems. Instead, conventionally, the railroads have relied on regular inspections and the observation of performance anomalies by the locomotive operator. Some cursory inspection processes are accomplished while the locomotive is in service; more thorough inspections require the locomotive to be taken out of service for several days. In any case, locomotive down time, whether for inspection or repair, represents a significant railroad cost. The avoidance of these costs by accurate fault diagnosis and prediction of potential failures represents an important cost saving opportunity for the railroads.
As a further means to reduce locomotive downtime, there has been a focus on the engineering design process with an objective of increasing the mean time between failures for locomotive subsystems and components. While this is certainly a commendable objective, it remains for the railroads to continue their cost containment goals through the collection and monitoring of real time performance data and fault related information directly from the locomotive, and the implementation of repairs before the problem requires significant down time.
The above-mentioned difficulties associated with locomotive operations can be ameliorated by the present invention, which relates to a novel and unobvious apparatus and method for measuring performance and fault-related parameters of the locomotive during operation. Monitoring the locomotive performance can provide timely and important indications of expected and immediate failures. With timely and continuous access to locomotive performance data, it is possible to predict and/or prevent untimely failures.
With recent advances in telecommunications technologies, it is now possible to collect information from a moving locomotive and transfer it to a fixed monitoring and diagnostic service center. With today""s advances in computing technology, the large amount of data collected from a fleet of locomotives can be properly aggregated and analyzed. The railroad can now better understand the operational and performance characteristics of its individual locomotives and the entire locomotive fleet. Analysis of this performance data can allow the railroad to advantageously predict and thereby avoid line-of-road failures.
The present invention provides for the collection, aggregation, and communication of locomotive performance and fault-related data from an operational locomotive. Generally, anomalous or fault conditions will be brought to the attention of the locomotive operator directly by the control system, but the control systems generally lack the necessary hardware and software elements to self-diagnose the fault. After collection, the performance data is communicated to a remote monitoring and diagnostic site, where data analysis tools operate on the data to identify the source of potential or actual faults. The analysis tools may employ case-based or artificial intelligence strategies. In addition to computer-based analysis, human operators who are experts in locomotive operation and maintenance analyze the data received. Historical data and patterns of anomalous behavior can be important clues to an accurate diagnosis and repair recommendation. The lessons learned from failure modes in a single locomotive can then be applied to other locomotives of the class or to the entire fleet so that the necessary preventative maintenance can be performed. When the data analysis process identifies incipient problems, certain performance aspects of the locomotive can be derated to avoid further system degradation, and further limit violation of operating parameters.